计算机工程与应用2018，Vol.54Issue(2)：68-75,8.DOI:10.3778/j.issn.1002-8331.1609-0112
面向不均衡分类的隶属度加权模糊支持向量机
摘要
Abstract
In view of the classification of imbalanced data set,a weighted fuzzy support vector machine is proposed,mak-ing use of the balanced adjustment factor and the fuzzy membership based on the features of samples.Firstly,it trains the classification hyperplane by traditional support vector machine and gets the fuzzy membership of every sample to be con-sidered as the contribution rate of every sample to eliminate the error caused by noises and outliers and subtract the num-ber of samples in a certain extent.Subsequently,it computes the balanced adjustment factor to alleviate the migration of hyperplane.Ultimately,experiments on a number of real-world data sets even including the data sets are imbalanced show that the proposed weighted fuzzy support vector machine algorithm is scalable and outperforms the existing fuzzy support vector machine as well as the typical support vector machine counterparts.关键词
模糊支持向量机/加权模糊支持向量机/分类超平面/模糊隶属度/平衡调节因子Key words
fuzzy support vector machine/weighted fuzzy support vector machine/classification hyperplane/fuzzy mem-bership/balanced adjustment factor分类
信息技术与安全科学引用本文复制引用
杨志民,王甜甜,邵元海..面向不均衡分类的隶属度加权模糊支持向量机[J].计算机工程与应用,2018,54(2):68-75,8.基金项目
国家自然科学基金(No.10926198) （No.10926198）
浙江省自然科学基金(No.LY16A010020). （No.LY16A010020）
